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Predicting students' performance: Educational data mining approach
University of Belgrade, Faculty of Organizational Sciences

emailsandro.radovanovic@hotmail.com
Keywords: Higher education; Performance prediction; Regression; Data Mining; Educational Data Mining
Abstract
Applying data mining on data gathered from educational environments is a new, growing research area also known as educational data mining (EDM). It is focused on developing models and methods for exploring data collected from educational environments. EDM considers different aspects of education: students, teachers, teaching materials, organization of classes in order to better understand and improve educational process. In this paper we use different data mining algorithms in order to find the best suited model for prediction of students' success at the end of their studies. These models are generated and evaluated on students' personal, high school, admission and first year grades data from Faculty of Organizational Sciences, University of Belgrade, who studied Information Systems and Technologies study program. Specifically, artificial neural networks, linear regression and support vector machines are applied on students' aforementioned data to generate the model, which can be used to predict the students' average grade at the end of their studies. Similarly, several attribute selection techniques are applied in order to identify which attributes contribute the most to prediction of students' performance. Experiments showed that genetic algorithm attribute weighting technique gave best results where absolute error for linear regression and support vector machines were 0.2528. Also, personal data does not influence the final grade average. On the other hand first year grades, except Economy course, admission and high school data are considered important.
References
Ayesha, S., Mustafa, T., Sattar, A.R., Khan, M.I. (2010) Data Mining Model for Higher Education System. European Journal of Scientific Research, str. 24-29
Bringula, R.P. (2013) Influence of faculty- and web portal design-related factors on web portal usability: A hierarchical regression analysis. Computers & Education, 68: 187-198
Delibasic, B., Vukicevic, M., Jovanovic, M. (2013) White-Box Decision Tree Algorithms: A Pilot Study on Perceived Usefulness, Perceived Ease of Use, and Perceived Understanding. International Journal of Engineering Education, str. 674-687
Delibasic, B., Vukicevic, M., Jovanovic, M., Suknovic, M. (2012) White-Box or Black-Box Decision Tree Algorithms: Which to Use in Education?. IEEE Transactions on Education, 56(3): 287-291
Garson, G.D. (1998) Neural networks: An introductory guide for social scientists. SAGE Publications Ltd
Han, J., Kamber, M. (2006) Data mining - concepts and tehniques. San Francisco: Elsevier
Hornik, K., Stinchcombe, M., White, H. (1989) Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359-366
Jovanovic, M., Vukicevic, M., Milovanovic, M., Minovic, M. (2012) Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study. International Journal of Computational Intelligence Systems, 5(3): 597-610
Jovanović, M., Vukićević, M., Isljamović, S., Suknović, M. (2012) Automatic evolutionary design of decision tree algorithm for prediction of university student success. in: Stochastic Modeling Techniques and Data Analysis International Conference, Chania, Greece
Jovanović, M., Vukicević, M., Isljamović, S., Delibašić, B., Suknović, M. (2012) Recommender system for selection of study program for higher education students. in: EURO 2012, Vilnius, Lithuania
Kotsiantis, S. B. (2012) Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review, 37(4): 331-344
Liao, S., Wen, C. (2007) Artificial neural networks classification and clustering of methodologies and applications - literature analysis from 1995 to 2005. Expert Systems with Applications, 32(1): 1-11
Minaei-Bidgoli, B., Punch, W.F. (2003) Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System. Lecture Notes in Computer Science, str. 2252-2263
Paliwal, M., Kumar, U.A. (2009) A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4): 7865-7872
Romero, C., Ventura, S. (2007) Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1): 135-146
Romero, C., Ventura, S., Espejo, P.G., Hervas, C. (2008) Data mining algorithms to classify students. in: Proceedings of Educational Data Mining, str. 8-17
Romero, C., Espejo, P.G., Zafra, A., Romero, J.R., Ventura, S. (2013) Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 21(1): 135-146
Romero, C., Ventura, S. (2013) Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1): 12-27
Seber, G., Lee, A. (2003) Linear Regression Analysis. New Jersey: John Wiley & Sons
Sison, R., Shimura, M. (1998) Student Modelling and Machine Learning. International Journal ofArtificial Intelligence in Education, (9): 128-158
Thomas, J., Chongwatpol, J., Pengnate, F., Hass, M. (2011) Data mining in higher education: University student declaration of major data mining in higher education. in: University student declaration of major, MWAIS 2011, Omaha, Nebraska
Top, E. (2012) Blogging as a social medium in undergraduate courses: Sense of community best predictor of perceived learning. Internet and Higher Education, 15(1): 24-28
Vapnik, V., Golowich, S.E., Smola, A. (1996) Support vector method for function approximation, regression estimation, and signal processing. in: Advances in Neural Information Processing Systems, MIT Press, 9 (pp. 281-287)
Vapnik, V.N. (1982) Estimation of dependences based on empirical data. Springer
 

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article language: English
document type: Original Scientific Paper
published in SCIndeks: 10/02/2014

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